Automated tumor assessment of squamous cell carcinoma on tongue cancer patients with hyperspectral imaging

Francesca Manni*, Fons Van Der Sommen, Sveta Zinger, Esther Kho, Susan Brouwer De Koning, Theo Ruers, Caifeng Shan, Jean Schleipen, Peter H.N. De With

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)
243 Downloads (Pure)


Head and neck cancer (HNC) includes cancers in the oral/nasal cavity, pharynx, larynx, etc., and it is the sixth most common cancer worldwide. The principal treatment is surgical removal where a complete tumor resection is crucial to reduce the recurrence and mortality rate. Intraoperative tumor imaging enables surgeons to objectively visualize the malignant lesion to maximize the tumor removal with healthy safe margins. Hyperspectral imaging (HSI) is an emerging imaging modality for cancer detection, which can augment surgical tumor inspection, currently limited to subjective visual inspection. In this paper, we aim to investigate HSI for automated cancer detection during image-guided surgery, because it can provide quantitative information about light interaction with biological tissues and exploit the potential for malignant tissue discrimination. The proposed solution forms a novel framework for automated tongue-cancer detection, explicitly exploiting HSI, which particularly uses the spectral variations in specific bands describing the cancerous tissue properties. The method follows a machine-learning based classification, employing linear support vector machine (SVM), and offers a superior sensitivity and a significant decrease in computation time. The model evaluation is on 7 ex-vivo specimens of squamous cell carcinoma of the tongue, with known histology. The HSI combined with the proposed classification reaches a sensitivity of 94%, specificity of 68% and area under the curve (AUC) of 92%. This feasibility study paves the way for introducing HSI as a non-invasive imaging aid for cancer detection and increase of the effectiveness of surgical oncology.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
ISBN (Electronic)9781510625495
Publication statusPublished - 1 Jan 2019
EventSPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - Town and Country Resort & Convention Center, San Diego, United States
Duration: 16 Feb 201921 Feb 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceSPIE Medical Imaging 2019
Country/TerritoryUnited States
CitySan Diego


  • Cancer detection
  • Hyperspectral imaging
  • Image classification
  • Image-guided surgery
  • Intraoperative tumor detection
  • Support vector machine
  • Tongue cancer


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